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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.01654 |
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| _version_ | 1866929236882751488 |
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| author | Tolnai, Balázs András Ma, Zheng Jørgensen, Bo Nørregaard |
| author_facet | Tolnai, Balázs András Ma, Zheng Jørgensen, Bo Nørregaard |
| contents | Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper con-ducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01654 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Scoping Review of Energy Load Disaggregation Tolnai, Balázs András Ma, Zheng Jørgensen, Bo Nørregaard Signal Processing Artificial Intelligence Computers and Society Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper con-ducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches. |
| title | A Scoping Review of Energy Load Disaggregation |
| topic | Signal Processing Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2402.01654 |